Optimization of Urban Traffic Microsimulation Model for Carbon Emission Reduction

doi:10.16182/j.issn1004731x.joss.22-1531

Abstract

Abstract:

To assess the environmental benefits of transportation management or control strategies, a method to effectively integrate the micro-traffic simulation model and the micro-vehicle emission model is proposed. VISSIM platform is used to build a case micro traffic simulation model. K-means clustering method is used to divide the driving behavior into 4 types based on the acceleration and deceleration characteristics of different speed intervals of the trajectory data, and the global parameters of the simulation model are calibrated based on the driving characteristics, which quantitatively describe the total sensitivity of the parameters and the sensitivity of the interactions between the parameters. The local parameter values are analyzed and calibrated by density-based spatial clustering of applications with noise(DBSCAN) clustering, which optimize the parameter calibration process. The simulated trajectory conditions are calculated, and motor vehicle emission simulator(MOVES) micro-emission model is localized.The emissions of HC, CO, NOx, and CO₂ pollutants under different flow directions and different driving behaviors at the intersection are obtained.Research results show that the optimization effect of the simulation model is remarkable. The proposed method can accurately identify the high-emission spatial locations and resolve the link between emissions and driving behavior. The application of DBSCAN cluster analysis parameter optimization value is conducive to the realization of the automatic calibration process. The calibration of global parameters reduces the $χ 2$ of velocity distribution from 0.632 7 to 0.130 6, and the $χ 2$ of acceleration distribution from 0.144 1 to 0.052 8, which is very important for the construction of simulation model from the environmental perspective.

Key words: traffic engineering, micro traffic simulation, quantitative emission, micro emission model, driving behavior, parameter calibration

CLC Number:

U491.92

Liu Bo, Lin Jianxin, Liu Yini, Zhang Dong. Optimization of Urban Traffic Microsimulation Model for Carbon Emission Reduction[J]. Journal of System Simulation, 2024, 36(4): 859-872.

Figures/Tables 21

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Table 1

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Table 2

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Table 3

Calculation results of index values of sensitivity analysis for all parameters

参数	S_i 一阶指数	排序	$S T i$ 总效应指数	排序
最大前视距离	0.10	9	0.41	6
观察到的车辆数	0	12	0	12
平均停车间距	0.51	2	0.53	4
安全距离附加部分	0.43	4	1.95	1
安全距离倍数部分	0.21	6	0.48	5
最大减速度	0.01	11	0.03	11
可接受减速度	0.56	1	0.57	3
-1 m/s²/距离	0.20	7	0.33	8
消失前等待时间	0.38	5	0.22	10
最小车头空距	0.06	10	0.26	9
安全距离折减系数	0.49	3	0.93	2
协调刹车最大减速	0.14	8	0.33	7

Table 3

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特征参数	主成分1	主成分2	主成分3
0~10 km/h平均加速度	-0.542	0.037	-0.416
0~10 km/h平均减速度	0.490	-0.283	0.638
10~20 km/h平均加速度	-0.549	0.227	0.027
10~20 km/h平均减速度	0.899	-0.146	0.097
20~30 km/h平均加速度	-0.577	-0.081	0.434
20~30 km/h平均减速度	0.756	0.063	-0.399
30~40 km/h平均加速度	-0.220	-0.538	0.011
30~40 km/h平均减速度	0.115	0.834	0.306
40~50 km/h平均加速度	-0.036	-0.583	0.185
40~50 km/h平均减速度	0.276	0.744	0.021

特征参数	驾驶类型Ⅰ	驾驶类型Ⅱ	驾驶类型Ⅲ	驾驶类型Ⅳ
0~10 km/h平均加速度	2.83	2.74	3.57	3.79
0~10 km/h平均减速度	-2.90	-2.78	-3.37	-3.92
10~20 km/h平均加速度	2.71	2.88	4.14	3.28
10~20 km/h平均减速度	-2.92	-3.04	-5.21	-4.31
20~30 km/h平均加速度	2.36	2.85	3.97	2.64
20~30 km/h平均减速度	-2.75	-3.00	-4.77	-3.40
30~40 km/h平均加速度	1.95	2.70	2.46	2.16
30~40 km/h平均减速度	-2.21	-3.56	-2.52	-2.82
40~50 km/h平均加速度	1.82	2.60	2.17	1.75
40~50 km/h平均减速度	-1.79	-2.93	-2.63	-2.20

参数	分散度	标定值
最大前视距离	0.245	329.78
平均停车间距	0.173	1.88
安全距离附加部分	0.308	1.76
安全距离倍数部分	0.362	2.47
可接受减速度	0.387	-1.51
安全距离折减系数	0.266	0.42

驾驶行为	HC	CO	NOx	CO₂
I	0.003 67	0.471 9	0.009 65	207.681 3
II	0.003 85	0.480 4	0.010 19	213.112 1
III	0.003 81	0.472 8	0.010 22	209.852 0
IV	0.003 80	0.479 5	0.009 77	211.871 4

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